A two-stage stochastic programming model for phlebotomist scheduling in hospital laboratories

Area of Science:

• Operations Research
• Healthcare Management
• Laboratory Science

Background:

• Phlebotomist scheduling in healthcare facilities presents challenges due to fluctuating patient demand.
• Inefficient scheduling can lead to staff work overload and impact laboratory efficiency.
• Optimizing phlebotomist deployment is crucial for effective healthcare delivery systems.

Purpose of the Study:

• To introduce a novel two-stage stochastic integer linear programming model for phlebotomist scheduling.
• To enable laboratory management to develop optimal scheduling policies that minimize phlebotomist work overload.
• To address the inherent uncertainty in blood collection demand within laboratory settings.

Main Methods:

• Development of a two-stage stochastic integer linear programming model.
• Incorporation of demand uncertainty into the scheduling optimization process.
• Application and case study analysis within a hospital laboratory setting.

Main Results:

• The proposed model provides a framework for optimizing phlebotomist schedules.
• Demonstrated potential to minimize work overload among laboratory phlebotomy staff.
• Successfully applied to a real-world hospital laboratory in North Carolina.

Conclusions:

• The stochastic programming model offers an effective approach to phlebotomist scheduling.
• Laboratory management can utilize this model to improve operational efficiency and staff well-being.
• The findings have significant implications for managing hospital laboratory resources.